# Chittron: An Automatic Bangla Image Captioning System

arXiv: https://arxiv.org/abs/1809.00339 · HTML: https://mlsanity.com/paper/1809.00339/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [Improved Bengali Image Captioning via deep convolutional neural network based encoder-decoder model](https://mlsanity.com/paper/2102.07192/) (2021)
- [Bornon: Bengali Image Captioning with Transformer-based Deep learning approach](https://mlsanity.com/paper/2109.05218/) (2021)
- [ChitroJera: A Regionally Relevant Visual Question Answering Dataset for Bangla](https://mlsanity.com/paper/2410.14991/) (2024)
- [BAN-Cap: A Multi-Purpose English-Bangla Image Descriptions Dataset](https://mlsanity.com/paper/2205.14462/) (2022)
- [Bangla Image Caption Generation through CNN-Transformer based Encoder-Decoder Network](https://mlsanity.com/paper/2110.12442/) (2021)
- [Polite on the Surface, Broken in Practice: A Curated Dataset for Fixing Generation and Register Failures in Low-Resource Bangla Text Generation](https://mlsanity.com/paper/2605.22487/) (2026)
- [Characterization and recognition of handwritten digits using Julia](https://mlsanity.com/paper/2102.11994/) (2021)
- [Image captioning in different languages](https://mlsanity.com/paper/2407.09495/) (2024)

## Abstract (arXiv)

Automatic image caption generation aims to produce an accurate description of an image in natural language automatically. However, Bangla, the fifth most widely spoken language in the world, is lagging considerably in the research and development of such domain. Besides, while there are many established data sets to related to image annotation in English, no such resource exists for Bangla yet. Hence, this paper outlines the development of "Chittron", an automatic image captioning system in Bangla. Moreover, to address the data set availability issue, a collection of 16,000 Bangladeshi contextual images has been accumulated and manually annotated in Bangla. This data set is then used to train a model which integrates a pre-trained VGG16 image embedding model with stacked LSTM layers. The model is trained to predict the caption when the input is an image, one word at a time. The results show that the model has successfully been able to learn a working language model and to generate captions of images quite accurately in many cases. The results are evaluated mainly qualitatively. However, BLEU scores are also reported. It is expected that a better result can be obtained with a bigger and more varied data set.